gbanyan
52612e14ba
Implement VLM name extraction + CV detection hybrid approach to
replace unreliable VLM coordinate system with name-based verification.
Key Features:
- VLM extracts signature names (周寶蓮, 魏興海, etc.)
- CV or PDF text layer detects regions
- VLM verifies each region against expected names
- Signatures saved with person names: signature_周寶蓮.png
- Duplicate prevention and rejection handling
Test Results:
- 5 PDF pages tested
- 7/10 signatures extracted (70% recall)
- 100% precision (no false positives)
- No blank regions extracted (previous issue resolved)
Files:
- extract_pages_from_csv.py: Extract pages from CSV (tested: 100 files)
- extract_signatures_hybrid.py: Hybrid extraction (current working solution)
- extract_handwriting.py: CV-only approach (component)
- extract_signatures_vlm.py: Deprecated VLM coordinate approach
- PROJECT_DOCUMENTATION.md: Complete project history and results
- SESSION_INIT.md: Session handoff documentation
- SESSION_CHECKLIST.md: Status checklist
- NEW_SESSION_PROMPT.txt: Template for next session
- HOW_TO_CONTINUE.txt: Visual handoff guide
- COMMIT_SUMMARY.md: Commit preparation guide
- README.md: Quick start guide
- README_page_extraction.md: Page extraction docs
- README_hybrid_extraction.md: Hybrid approach docs
- .gitignore: Exclude diagnostic scripts and outputs
Known Limitations:
- 30% of signatures missed due to conservative CV parameters
- Text layer method untested (all test PDFs are scanned images)
- Performance: ~24 seconds per PDF
Next Steps:
- Tune CV parameters for higher recall
- Test with larger dataset (100+ files)
- Process full dataset (86,073 files)
🤖 Generated with Claude Code
36 lines
1.3 KiB
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36 lines
1.3 KiB
Plaintext
I'm continuing work on the PDF signature extraction project at /Volumes/NV2/pdf_recognize/
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Please read these files to understand the current state:
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1. /Volumes/NV2/pdf_recognize/SESSION_INIT.md (start here)
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2. /Volumes/NV2/pdf_recognize/PROJECT_DOCUMENTATION.md (complete history)
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Key context:
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- Working hybrid approach: VLM name extraction + CV detection + VLM verification
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- Test results: 70% recall, 100% precision (5 PDFs tested)
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- Important: VLM coordinates are unreliable (32% offset discovered), we use names instead
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- Current script: extract_signatures_hybrid.py
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I want to: [CHOOSE ONE OR DESCRIBE YOUR GOAL]
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Option A: Improve recall from 70% to 90%+
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- Tune CV detection parameters to catch more signatures
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- Test if missing signatures are in rejected folder
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Option B: Scale up testing to 100 PDFs
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- Verify reliability on larger dataset
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- Analyze results and calculate overall metrics
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Option C: Commit current solution to git
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- Follow instructions in COMMIT_SUMMARY.md
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- Tag release as v1.0-hybrid-70percent
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Option D: Process full dataset (86,073 files)
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- Estimate time and optimize if needed
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- Set up monitoring and resume capability
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Option E: Debug specific issue
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- [Describe the issue you're encountering]
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Option F: Other
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- [Describe what you want to work on]
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